June 19, 2025 • 16 mins
This deep dive explains how artificial intelligence (AI) is integrated into various aspects of daily life, often subtly impacting our decisions. It is based on an article titled "Artificial Intelligence In Everyday Decision Making". It defines AI as using data to replicate human intelligence for tasks like problem-solving and learning. The text highlights everyday applications such as digital assistants, personalized online shopping experiences, and how AI affects both personal choices and business strategies, emphasizing the benefits and potential challenges of this pervasive technology. Find out more, and read the full article here
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Episode Transcript
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Speaker 1 (00:00):
Okay, think about this morning. First thing you did, maybe
check your phone, or maybe later browsing online and those
suggestions pop up, you know the ones. They feel almost
creepily perfect.
Speaker 2 (00:13):
Yeah, I know exactly what you mean.
Speaker 1 (00:14):
Well, you've already had a run in with artificial intelligence.
It's this like invisible force that's quietly shaping so much
of what we do.
Speaker 2 (00:22):
That's spot on, and in this deep dive, we're going
to try and unpack exactly how AI has become so
woven into well the fabric of our everyday decisions, often
without us even clocking it exactly.
Speaker 1 (00:35):
So our mission today is really to, you know, pull
back the curtain on all this. We'll pick off with
a clearer idea of what AI actually is, what's the
place to start. Then we'll explore it's different flavors, you
could say, look at some examples you bump into constantly,
and then finally really talk about the impact it's having
on the choices we make, personally, professionally, all of it.
Speaker 2 (00:54):
The goal being by the end you'll hopefully have a
much sharper picture of AI's role and maybe feel a
bit more equipped to navigate this tech landscape we're in.
Speaker 1 (01:03):
Couldn't have set it better. Okay, let's get into it.
What do we actually mean when we say artificial intelligence?
It gets thrown around a lot.
Speaker 2 (01:11):
It really does, so at its heart, artificial intelligence is
basically about teaching machines to handle tasks that well typically
needs human smarts.
Speaker 1 (01:21):
Human smarts like things.
Speaker 2 (01:22):
Like solving problems or learning from experience, understanding language, making decisions,
and probably worth saying up front, you know the whole
movie trope Robots taking over the world. Yeah, it's still
very much sci fi, not quite the reality of AI today.
Speaker 1 (01:36):
Definitely no robot uprising plan for this afternoon, then good
to know. So if it's not about killer robots, what's
the engine behind this intelligence? What makes it tick?
Speaker 2 (01:46):
The real engine is data, Just massive, massive amounts of data,
right data. Yeah, this data is what AI systems chew
on to find patterns, to learn things and draw conclusions.
So essentially AI helps create systems that can look at
all this information and then make decisions based on what
they find, often without needing super detailed explicit instructions for
(02:06):
every single step.
Speaker 1 (02:07):
Ah. Okay, so it's not just like a really complicated
flow chart. It's actually learning, adapting based on what.
Speaker 2 (02:14):
It's seeing precisely, it learns and adapts, and you know,
the whole field of AI, it's a branch of computer science.
The big aim is to create systems that can kind
of mimic human thought processes using what we call algorithms.
Think of algorithms as like the recipes or the instruction
sets that tell the AI how to process data and
(02:34):
make those decisions. And this is what enables all those
real time decisions we see happening everywhere now.
Speaker 1 (02:39):
Right, Okay, so AI is this big umbrella term, but
I know it's not all the same thing. They're different kinds,
different branches, right, Can we dig into those a bit?
Speaker 2 (02:48):
Absolutely? Yeah. It's really helpful to think of AI not
as one single thing, but more like a family of
related technologies and approaches. One of the biggest ones you
hear about constantly is machine learning.
Speaker 1 (03:00):
Machine learning. Yes, that term is everywhere. What's the basic
idea there? Is it? What it sounds like? Machines learning?
Speaker 2 (03:06):
Pretty much? The core idea is instead of programmers writing
code for every single possible action or decision.
Speaker 3 (03:14):
We feed these systems tons and tons of data and
they literally learn from that data. They get good at
spotting patterns, making predictions based on what they've seen before.
Interesting and within mL, there are different ways this learning happens.
You've got supervised learning I think like a student learning
with a teacher providing labeled examples. Then there's unsupervised learning,
(03:36):
which is more about the system finding hidden structures in
data all by itself, no labels. And reinforcement learning that's
like learning through trial and error, getting rewarded for good moves,
penalized for bad ones until it figures out the best
strategy to reach a goal. Okay, supervised unsupervised reinforcement. That
clarifies mL a lot. Yeah, So mL is about learning
(03:58):
from data. What about this narrow AI? That sounds limited?
Speaker 2 (04:02):
It is, and that's the point. Actually, narrow AI, sometimes
called weak AI, is designed to be absolutely brilliant at
one specific task. Yeah, or maybe have a very small
set of tasks, like what think about the virtual assistant
on your phone or the recommendation engine on your favorite
streaming service. They are incredibly good within their defined.
Speaker 1 (04:23):
Area, scaily good, sometimes.
Speaker 2 (04:25):
Exactly, but they can't step outside that box. Your voice
assistant is great for setting timers or playing music, but
it can't understand complex emotions or write a novel.
Speaker 1 (04:35):
Right, it stays in its lane. So my assistant isn't
going to suddenly start offering me life advice.
Speaker 2 (04:40):
Probably not now. The sort of flip side or maybe
the ultimate goal for some researchers is artificial general intelligence AGI.
That sounds bigger.
Speaker 1 (04:51):
It is. This is the long term ambition to build
machines that have human like understanding, learning capabilities, the ability
to apply knowledge flexibly across tons of different tasks, just
like a person can't. Ah.
Speaker 2 (05:02):
So that's closer to the sci fi stuff.
Speaker 1 (05:04):
In concept, maybe, but it's a vastly more complex challenge
than movies often suggest. We're still very much in the
research phase with AGI. It's not something you're interacting with
day to day.
Speaker 2 (05:13):
Gotcha. So mL narrow AI, AGI. Any other key branches
we should know about, Definitely. Another really crucial one is
natural language processing. Yeah NLP.
Speaker 1 (05:24):
NLP, that sounds familiar. That's about computers understanding us, like
our language exactly. That.
Speaker 2 (05:30):
NLP is all about enabling computers to understand, interpret, and
even generate human language spoken or written.
Speaker 1 (05:38):
Which must be incredibly hard. Language is messy, Oh.
Speaker 2 (05:42):
It's incredibly complex. Think about sarcasm context, different meanings for
the same word. But NLP is the magic behind chatbots,
those voice assistance we mentioned translation tools, even spam filters
in your email.
Speaker 1 (05:55):
Wow, okay, it powers a lot of things we almost
take for granted.
Speaker 2 (05:59):
Now it really does.
Speaker 1 (06:00):
It's kind of wild to think how far this has
all come. You mentioned history earlier. Any key moments that
stand out?
Speaker 2 (06:05):
Oh? Sure, the history is fascinating. Really. People often point
back to Alan Turing in the mid twentieth century. His
work laid a lot of the theoretical groundwork.
Speaker 1 (06:15):
Codebreaker guy, that's him.
Speaker 2 (06:17):
Then a really pivotal moment was the Dartmouth Workshop back
in nineteen fifty six. That's often considered the sort of
official birth of AI as its own field of research.
There has been this journey of fits and starts, breakthroughs,
and constant innovation ever since.
Speaker 1 (06:32):
Okay, great, so we've got a much better handle on
what AI is, the different type, the role of data.
Now let's get really practical. Where are we bumping into
this stuff in our daily lives? You said it's often
hidden in plain sight.
Speaker 2 (06:44):
That's absolutely the key. It's become so smoothly integrated it
often just works away in the background. Digital assistants are
probably the most obvious example from.
Speaker 1 (06:51):
Many people, right Siri, Alexa, Google Assistant, the voices in
the machines. They seem to be everywhere.
Speaker 2 (06:58):
Now they really are, and they've shifted from being maybe
novelties a few years ago to genuinely useful tools for
managing our daily grind. Oh so well. People use them
constantly for organizing schedules, setting reminders remind me to call
Mum later, Controlling smart home devices like lights or thermostats. Yeah,
just think about it. Setting a cooking timer when your
hands are covered in flour, asking for your favorite playlist
(07:21):
while you're getting ready, or just check in the weather
forecast without even picking up your phone.
Speaker 1 (07:26):
I have to admit I use mine all the time
for kitchen timers and music. It's just easy, surprisingly convenient,
almost like having an extra pair of hands sometimes.
Speaker 2 (07:35):
And that ease of use, that intuitive interaction, that's all
down to that natural language processing the NLP.
Speaker 1 (07:40):
We just talked about, a connecting the dots.
Speaker 2 (07:42):
The assistant has to understand what you said, figure out
the intent behind it, and then generate a response or
perform the action.
Speaker 1 (07:50):
You know. It's funny, thinking back, I was pretty skeptical
about smart speakers at first, seemed like another gadget I
didn't need.
Speaker 2 (07:56):
Mm hmm, I remember that phase.
Speaker 1 (07:58):
But now yeah, like you said, it's hard to imagine
not having that quick voice activated way to ask a
question or set that timer when I'm busy.
Speaker 2 (08:07):
It really demonstrates how quickly these technologies can bed themselves
into our routines once they prove their convenience factor.
Speaker 1 (08:14):
Definitely. Okay, So assistants are one big area. Where else
is AI heavily involved in our day to day?
Speaker 2 (08:20):
Another massive one is online shopping. It's completely transformed how
we browse and buy.
Speaker 1 (08:26):
Oh yeah, the infamous recommendation algorithms. Sometimes it feels like
they know what I want before I do.
Speaker 2 (08:31):
It can feel uncanny, can it?
Speaker 1 (08:33):
Yeah?
Speaker 2 (08:33):
Those recommendation engines. Whether you're on a giant platform like
Amazon or a niche clothing site like a sauce, they're
powered by AI.
Speaker 1 (08:42):
How do they work? What are they looking at?
Speaker 2 (08:44):
They're analyzing tons of data about you, your past browsing history,
things you've actually bought, maybe items you've put on a
wish list, even how long you looked at certain products. Wow,
And they use all that to build a profile and
create this stream of personalized suggestion and things they predict
you'll be interested in.
Speaker 1 (09:01):
It's kind of a double edged sword, though, isn't it. Like,
on one hand, cool I discover new things I might
genuinely like. But on the other hand, wow, does it
make it easy to just click by?
Speaker 2 (09:10):
Now that's a really sharp observation about how technology influences
our behavior, sometimes subtly nudging us. But AI in shopping
isn't just about recommendations. Oh, it's also working behind the
scenes in ways you might not realize, like fraud prevention.
AI systems are constantly scanning transactions looking for patterns that
(09:30):
might indicate something fishy, something fraudulent.
Speaker 1 (09:33):
Okay, that's definitely useful, And.
Speaker 2 (09:35):
It's even involved in dynamic pricing. Retailers sometimes use AI
to adjust prices on the fly based on factors like demand,
competitor pricing, even the time of day.
Speaker 1 (09:45):
Seriously, so the price I see might not be the
same price someone else sees, or even the price I
saw yesterday.
Speaker 2 (09:50):
It's possible. Yes, it's all about optimizing. So AI is
making the experience smoother and more secure in some ways.
Speaker 1 (09:57):
So just to recap for online shopping, doing the recommendations.
It might be adjusting the prices. It's helping detect fraud,
and presumably improving the search function when I'm looking for
something specific.
Speaker 2 (10:08):
Exactly, You've got it. Better search results, personalized suggestions, dynamic pricing,
and enhanced security, all driven by AI analyzing data patterns.
Speaker 1 (10:18):
Okay, so AI is clearly deeply embedded in these everyday
things like talking to assistance and shopping online. Let's zoomilt
a bit. Now, what's the bigger picture? How is all
this actually influencing the decisions we make, both in our
personal lives and maybe in the business world too.
Speaker 2 (10:32):
Right, this is where it gets really fascinating and maybe
a little complex. AI's influence on our actual choices. Our
decision making is becoming much more direct and noticeable.
Speaker 1 (10:43):
How so in our personal lives we touched on recommendations.
Speaker 2 (10:47):
Yeah, exactly. Think about those nudges from streaming services. Because
you watched X, you might like why that's AI shaping
your entertainment choices or navigation apps?
Speaker 1 (10:58):
Oh yeah, Google maps ways.
Speaker 2 (11:00):
Show you the map. They actively suggest the best route,
factoring in real time traffic accidents, road closures. They're literally
guiding your physical movement through the world based on AI analysis.
Speaker 1 (11:10):
It feels efficient. But are we maybe handing over too
much of our own judgment? Like, are we becoming too
reliant on these suggestions? Could it? I don't know, dull
our own critical thinking skills.
Speaker 2 (11:21):
That's the million dollar question, isn't it? And it's something
people are increasingly debating. Let's look at some specific examples.
We have personalized recommendations, like we said, influencing what we buy,
watch read, even who we connect with online. Then there's
health and wellness wearable devices, fitness trackers. Many use AI
to analyze your activity levels, your sleep patterns, maybe even
(11:43):
estimate stress levels, and then offer advice or nudges like.
Speaker 1 (11:47):
Time to stand up or maybe take some deep breadths.
Speaker 2 (11:50):
Exactly yeah, or suggesting workout goals. And think about personal finance.
There are AI powered apps now that analyze your spending,
help you budget track in best, even provide automated financial
advice or portfolio suggestions.
Speaker 1 (12:03):
Wow, okay, so it's filtering into our entertainment, our travel,
or health, our finances pretty much everywhere.
Speaker 2 (12:09):
It really is touching almost every aspect. But and this
is crucial, it goes back to what you said earlier.
It's a tool, right, not destiny precisely, and like any
powerful tool, it can be used well or maybe not
so well. The key is awareness, understanding its limitations, potential
biases in the data it learned from, and consciously deciding
(12:29):
how much influence we want to give it. Yeah, we
need to stay in the driver's seat essentially.
Speaker 1 (12:33):
So maintain that critical thinking, don't just blindly follow the algorithm.
Makes sense. What about the flip side. How is AI
impacting decisions in the business world, strategies, operations.
Speaker 2 (12:45):
Oh, in business AI is arguably having any even bigger,
more structural impact. It's becoming a real game changer for
many companies in what way they're using it to automate
routine tasks which freeze up employees for more complex work.
They're using it to become way more fit their operations,
and ultimately they're using it to try and gain a
competitive edge.
Speaker 1 (13:04):
Oh, give me some examples.
Speaker 2 (13:06):
Well. AI algorithms can sift through enormous data sets, market trends,
customer feedback, competitor activities much faster than humans can. They
can spot subtle patterns or predict future customer behavior, and
that allows businesses to make more informed decisions about say
product development, marketing campaigns, or optimizing their pricing strategies in
real time.
Speaker 1 (13:26):
Which ideally leads to better business outcomes like increased profits.
Maybe happier customers if the products get better that's the goal.
Speaker 2 (13:34):
Yes, happier customers, more efficient operations, better bottom lines. If
you look at the historical arc, AI in business has
gone from being kind of experimental to being pretty central
for many industries.
Speaker 1 (13:45):
Can you break down some specific applications like how are
businesses actually using it day to day?
Speaker 2 (13:50):
Sure? Data analysis is a massive one as I mentioned, Yeah,
AI can find those needles in the haystack, valuable insights
hidden in vast amounts of data that would just be
noise to a human analyst.
Speaker 1 (14:01):
Right processing power.
Speaker 2 (14:02):
Exactly, automations and other key area think about tasks like
data entry, scheduling, basic customer inquiries. AI can handle a
lot of that, freeing up human staff for things requiring creativity,
empathy or complex problem solving.
Speaker 1 (14:15):
Makes sense? What else?
Speaker 2 (14:16):
And customer service is huge too. Chatbots virtual assistance. They
can provide instant twenty four to seven support, answer common questions,
guide users through processes. This can seriously improve customer satisfaction
and reduce way times.
Speaker 1 (14:30):
Okay, I can see how that would be attractive for
a business.
Speaker 2 (14:33):
Absolutely so if we bring it all together, AI is
constantly analyzing data and making these suggestions or automating processes.
This subtly shapes the choices we make as consumers, what
we see, what we buy, how we get information, and
for businesses, it's giving them powerful tools to understand us
their customers better than ever before and hopefully create products
(14:54):
and services that we actually want or need.
Speaker 1 (14:56):
It really connects back to that data being the fuel.
Speaker 2 (14:58):
It always comes back to the data. And you know,
for anyone listening who's curious about diving deeper into how
AI could specifically help them or their business, there are
loads of resources online now, websites, tools, articles that offer
more specific insights.
Speaker 1 (15:12):
Good tip. Okay, let's try and wrap this up. We've
covered a lot of ground in this deep dive. It
feels like the main takeaway is just how pervasive AI
has become. It's this incredibly widespread, influential force in our
daily lives, but often operating you know, almost invisibly behind
the scenes.
Speaker 2 (15:28):
Absolutely from the moment you pick up your phone in
the morning, through your commute your work, you're online shopping,
how you unwind in the evening. AI is likely playing
a role subtly guiding choices and shaping experiences.
Speaker 1 (15:41):
Yeah, it's really not some distant futuristic idea anymore, is it.
It's very much a present day reality, and it is
simplifying and streamlining lots of things.
Speaker 2 (15:49):
It definitely offers significant benefits in convenience efficiency, personalization, though,
as we discuss, it's also fair to have conversations about
things like privacy bias and that risk of overreliance.
Speaker 1 (16:02):
Important caveats, But the bottom line is AI is here,
and it's deeply integrated.
Speaker 2 (16:07):
And looking ahead, it seems almost certain that its role
in our everyday decisions is only going to grow. It'll
likely become even more seamless, even more integrated.
Speaker 1 (16:16):
So maybe a final thought for everyone listening today just
try this, Yeah, think about one specific instance from your
day so far, just one moment where, now that you've
heard all this, you realize AI probably played a part
in a decision you made or an experience you had.
Speaker 2 (16:31):
Hmm, yeah, good exercise.
Speaker 1 (16:33):
It could be tiny a song recommendation you skipped or
listened to and add that seemed relevant that slightly quicker
route your GPS found. Just pick one thing and pause
on it for a second.
Speaker 2 (16:44):
It might just shift your perspective a little bit on
your own relationship with this technology that's quietly reshaping our world.
Speaker 1 (16:50):
Exactly something to mull over. Thanks for that fascinating discussion,
My pleasure.
Speaker 2 (16:55):
Lots to think about.
Okay, think about this morning. First thing you did, maybe
check your phone, or maybe later browsing online and those
suggestions pop up, you know the ones. They feel almost
creepily perfect.
Speaker 2 (00:13):
Yeah, I know exactly what you mean.
Speaker 1 (00:14):
Well, you've already had a run in with artificial intelligence.
It's this like invisible force that's quietly shaping so much
of what we do.
Speaker 2 (00:22):
That's spot on, and in this deep dive, we're going
to try and unpack exactly how AI has become so
woven into well the fabric of our everyday decisions, often
without us even clocking it exactly.
Speaker 1 (00:35):
So our mission today is really to, you know, pull
back the curtain on all this. We'll pick off with
a clearer idea of what AI actually is, what's the
place to start. Then we'll explore it's different flavors, you
could say, look at some examples you bump into constantly,
and then finally really talk about the impact it's having
on the choices we make, personally, professionally, all of it.
Speaker 2 (00:54):
The goal being by the end you'll hopefully have a
much sharper picture of AI's role and maybe feel a
bit more equipped to navigate this tech landscape we're in.
Speaker 1 (01:03):
Couldn't have set it better. Okay, let's get into it.
What do we actually mean when we say artificial intelligence?
It gets thrown around a lot.
Speaker 2 (01:11):
It really does, so at its heart, artificial intelligence is
basically about teaching machines to handle tasks that well typically
needs human smarts.
Speaker 1 (01:21):
Human smarts like things.
Speaker 2 (01:22):
Like solving problems or learning from experience, understanding language, making decisions,
and probably worth saying up front, you know the whole
movie trope Robots taking over the world. Yeah, it's still
very much sci fi, not quite the reality of AI today.
Speaker 1 (01:36):
Definitely no robot uprising plan for this afternoon, then good
to know. So if it's not about killer robots, what's
the engine behind this intelligence? What makes it tick?
Speaker 2 (01:46):
The real engine is data, Just massive, massive amounts of data,
right data. Yeah, this data is what AI systems chew
on to find patterns, to learn things and draw conclusions.
So essentially AI helps create systems that can look at
all this information and then make decisions based on what
they find, often without needing super detailed explicit instructions for
(02:06):
every single step.
Speaker 1 (02:07):
Ah. Okay, so it's not just like a really complicated
flow chart. It's actually learning, adapting based on what.
Speaker 2 (02:14):
It's seeing precisely, it learns and adapts, and you know,
the whole field of AI, it's a branch of computer science.
The big aim is to create systems that can kind
of mimic human thought processes using what we call algorithms.
Think of algorithms as like the recipes or the instruction
sets that tell the AI how to process data and
(02:34):
make those decisions. And this is what enables all those
real time decisions we see happening everywhere now.
Speaker 1 (02:39):
Right, Okay, so AI is this big umbrella term, but
I know it's not all the same thing. They're different kinds,
different branches, right, Can we dig into those a bit?
Speaker 2 (02:48):
Absolutely? Yeah. It's really helpful to think of AI not
as one single thing, but more like a family of
related technologies and approaches. One of the biggest ones you
hear about constantly is machine learning.
Speaker 1 (03:00):
Machine learning. Yes, that term is everywhere. What's the basic
idea there? Is it? What it sounds like? Machines learning?
Speaker 2 (03:06):
Pretty much? The core idea is instead of programmers writing
code for every single possible action or decision.
Speaker 3 (03:14):
We feed these systems tons and tons of data and
they literally learn from that data. They get good at
spotting patterns, making predictions based on what they've seen before.
Interesting and within mL, there are different ways this learning happens.
You've got supervised learning I think like a student learning
with a teacher providing labeled examples. Then there's unsupervised learning,
(03:36):
which is more about the system finding hidden structures in
data all by itself, no labels. And reinforcement learning that's
like learning through trial and error, getting rewarded for good moves,
penalized for bad ones until it figures out the best
strategy to reach a goal. Okay, supervised unsupervised reinforcement. That
clarifies mL a lot. Yeah, So mL is about learning
(03:58):
from data. What about this narrow AI? That sounds limited?
Speaker 2 (04:02):
It is, and that's the point. Actually, narrow AI, sometimes
called weak AI, is designed to be absolutely brilliant at
one specific task. Yeah, or maybe have a very small
set of tasks, like what think about the virtual assistant
on your phone or the recommendation engine on your favorite
streaming service. They are incredibly good within their defined.
Speaker 1 (04:23):
Area, scaily good, sometimes.
Speaker 2 (04:25):
Exactly, but they can't step outside that box. Your voice
assistant is great for setting timers or playing music, but
it can't understand complex emotions or write a novel.
Speaker 1 (04:35):
Right, it stays in its lane. So my assistant isn't
going to suddenly start offering me life advice.
Speaker 2 (04:40):
Probably not now. The sort of flip side or maybe
the ultimate goal for some researchers is artificial general intelligence AGI.
That sounds bigger.
Speaker 1 (04:51):
It is. This is the long term ambition to build
machines that have human like understanding, learning capabilities, the ability
to apply knowledge flexibly across tons of different tasks, just
like a person can't. Ah.
Speaker 2 (05:02):
So that's closer to the sci fi stuff.
Speaker 1 (05:04):
In concept, maybe, but it's a vastly more complex challenge
than movies often suggest. We're still very much in the
research phase with AGI. It's not something you're interacting with
day to day.
Speaker 2 (05:13):
Gotcha. So mL narrow AI, AGI. Any other key branches
we should know about, Definitely. Another really crucial one is
natural language processing. Yeah NLP.
Speaker 1 (05:24):
NLP, that sounds familiar. That's about computers understanding us, like
our language exactly. That.
Speaker 2 (05:30):
NLP is all about enabling computers to understand, interpret, and
even generate human language spoken or written.
Speaker 1 (05:38):
Which must be incredibly hard. Language is messy, Oh.
Speaker 2 (05:42):
It's incredibly complex. Think about sarcasm context, different meanings for
the same word. But NLP is the magic behind chatbots,
those voice assistance we mentioned translation tools, even spam filters
in your email.
Speaker 1 (05:55):
Wow, okay, it powers a lot of things we almost
take for granted.
Speaker 2 (05:59):
Now it really does.
Speaker 1 (06:00):
It's kind of wild to think how far this has
all come. You mentioned history earlier. Any key moments that
stand out?
Speaker 2 (06:05):
Oh? Sure, the history is fascinating. Really. People often point
back to Alan Turing in the mid twentieth century. His
work laid a lot of the theoretical groundwork.
Speaker 1 (06:15):
Codebreaker guy, that's him.
Speaker 2 (06:17):
Then a really pivotal moment was the Dartmouth Workshop back
in nineteen fifty six. That's often considered the sort of
official birth of AI as its own field of research.
There has been this journey of fits and starts, breakthroughs,
and constant innovation ever since.
Speaker 1 (06:32):
Okay, great, so we've got a much better handle on
what AI is, the different type, the role of data.
Now let's get really practical. Where are we bumping into
this stuff in our daily lives? You said it's often
hidden in plain sight.
Speaker 2 (06:44):
That's absolutely the key. It's become so smoothly integrated it
often just works away in the background. Digital assistants are
probably the most obvious example from.
Speaker 1 (06:51):
Many people, right Siri, Alexa, Google Assistant, the voices in
the machines. They seem to be everywhere.
Speaker 2 (06:58):
Now they really are, and they've shifted from being maybe
novelties a few years ago to genuinely useful tools for
managing our daily grind. Oh so well. People use them
constantly for organizing schedules, setting reminders remind me to call
Mum later, Controlling smart home devices like lights or thermostats. Yeah,
just think about it. Setting a cooking timer when your
hands are covered in flour, asking for your favorite playlist
(07:21):
while you're getting ready, or just check in the weather
forecast without even picking up your phone.
Speaker 1 (07:26):
I have to admit I use mine all the time
for kitchen timers and music. It's just easy, surprisingly convenient,
almost like having an extra pair of hands sometimes.
Speaker 2 (07:35):
And that ease of use, that intuitive interaction, that's all
down to that natural language processing the NLP.
Speaker 1 (07:40):
We just talked about, a connecting the dots.
Speaker 2 (07:42):
The assistant has to understand what you said, figure out
the intent behind it, and then generate a response or
perform the action.
Speaker 1 (07:50):
You know. It's funny, thinking back, I was pretty skeptical
about smart speakers at first, seemed like another gadget I
didn't need.
Speaker 2 (07:56):
Mm hmm, I remember that phase.
Speaker 1 (07:58):
But now yeah, like you said, it's hard to imagine
not having that quick voice activated way to ask a
question or set that timer when I'm busy.
Speaker 2 (08:07):
It really demonstrates how quickly these technologies can bed themselves
into our routines once they prove their convenience factor.
Speaker 1 (08:14):
Definitely. Okay, So assistants are one big area. Where else
is AI heavily involved in our day to day?
Speaker 2 (08:20):
Another massive one is online shopping. It's completely transformed how
we browse and buy.
Speaker 1 (08:26):
Oh yeah, the infamous recommendation algorithms. Sometimes it feels like
they know what I want before I do.
Speaker 2 (08:31):
It can feel uncanny, can it?
Speaker 1 (08:33):
Yeah?
Speaker 2 (08:33):
Those recommendation engines. Whether you're on a giant platform like
Amazon or a niche clothing site like a sauce, they're
powered by AI.
Speaker 1 (08:42):
How do they work? What are they looking at?
Speaker 2 (08:44):
They're analyzing tons of data about you, your past browsing history,
things you've actually bought, maybe items you've put on a
wish list, even how long you looked at certain products. Wow,
And they use all that to build a profile and
create this stream of personalized suggestion and things they predict
you'll be interested in.
Speaker 1 (09:01):
It's kind of a double edged sword, though, isn't it. Like,
on one hand, cool I discover new things I might
genuinely like. But on the other hand, wow, does it
make it easy to just click by?
Speaker 2 (09:10):
Now that's a really sharp observation about how technology influences
our behavior, sometimes subtly nudging us. But AI in shopping
isn't just about recommendations. Oh, it's also working behind the
scenes in ways you might not realize, like fraud prevention.
AI systems are constantly scanning transactions looking for patterns that
(09:30):
might indicate something fishy, something fraudulent.
Speaker 1 (09:33):
Okay, that's definitely useful, And.
Speaker 2 (09:35):
It's even involved in dynamic pricing. Retailers sometimes use AI
to adjust prices on the fly based on factors like demand,
competitor pricing, even the time of day.
Speaker 1 (09:45):
Seriously, so the price I see might not be the
same price someone else sees, or even the price I
saw yesterday.
Speaker 2 (09:50):
It's possible. Yes, it's all about optimizing. So AI is
making the experience smoother and more secure in some ways.
Speaker 1 (09:57):
So just to recap for online shopping, doing the recommendations.
It might be adjusting the prices. It's helping detect fraud,
and presumably improving the search function when I'm looking for
something specific.
Speaker 2 (10:08):
Exactly, You've got it. Better search results, personalized suggestions, dynamic pricing,
and enhanced security, all driven by AI analyzing data patterns.
Speaker 1 (10:18):
Okay, so AI is clearly deeply embedded in these everyday
things like talking to assistance and shopping online. Let's zoomilt
a bit. Now, what's the bigger picture? How is all
this actually influencing the decisions we make, both in our
personal lives and maybe in the business world too.
Speaker 2 (10:32):
Right, this is where it gets really fascinating and maybe
a little complex. AI's influence on our actual choices. Our
decision making is becoming much more direct and noticeable.
Speaker 1 (10:43):
How so in our personal lives we touched on recommendations.
Speaker 2 (10:47):
Yeah, exactly. Think about those nudges from streaming services. Because
you watched X, you might like why that's AI shaping
your entertainment choices or navigation apps?
Speaker 1 (10:58):
Oh yeah, Google maps ways.
Speaker 2 (11:00):
Show you the map. They actively suggest the best route,
factoring in real time traffic accidents, road closures. They're literally
guiding your physical movement through the world based on AI analysis.
Speaker 1 (11:10):
It feels efficient. But are we maybe handing over too
much of our own judgment? Like, are we becoming too
reliant on these suggestions? Could it? I don't know, dull
our own critical thinking skills.
Speaker 2 (11:21):
That's the million dollar question, isn't it? And it's something
people are increasingly debating. Let's look at some specific examples.
We have personalized recommendations, like we said, influencing what we buy,
watch read, even who we connect with online. Then there's
health and wellness wearable devices, fitness trackers. Many use AI
to analyze your activity levels, your sleep patterns, maybe even
(11:43):
estimate stress levels, and then offer advice or nudges like.
Speaker 1 (11:47):
Time to stand up or maybe take some deep breadths.
Speaker 2 (11:50):
Exactly yeah, or suggesting workout goals. And think about personal finance.
There are AI powered apps now that analyze your spending,
help you budget track in best, even provide automated financial
advice or portfolio suggestions.
Speaker 1 (12:03):
Wow, okay, so it's filtering into our entertainment, our travel,
or health, our finances pretty much everywhere.
Speaker 2 (12:09):
It really is touching almost every aspect. But and this
is crucial, it goes back to what you said earlier.
It's a tool, right, not destiny precisely, and like any
powerful tool, it can be used well or maybe not
so well. The key is awareness, understanding its limitations, potential
biases in the data it learned from, and consciously deciding
(12:29):
how much influence we want to give it. Yeah, we
need to stay in the driver's seat essentially.
Speaker 1 (12:33):
So maintain that critical thinking, don't just blindly follow the algorithm.
Makes sense. What about the flip side. How is AI
impacting decisions in the business world, strategies, operations.
Speaker 2 (12:45):
Oh, in business AI is arguably having any even bigger,
more structural impact. It's becoming a real game changer for
many companies in what way they're using it to automate
routine tasks which freeze up employees for more complex work.
They're using it to become way more fit their operations,
and ultimately they're using it to try and gain a
competitive edge.
Speaker 1 (13:04):
Oh, give me some examples.
Speaker 2 (13:06):
Well. AI algorithms can sift through enormous data sets, market trends,
customer feedback, competitor activities much faster than humans can. They
can spot subtle patterns or predict future customer behavior, and
that allows businesses to make more informed decisions about say
product development, marketing campaigns, or optimizing their pricing strategies in
real time.
Speaker 1 (13:26):
Which ideally leads to better business outcomes like increased profits.
Maybe happier customers if the products get better that's the goal.
Speaker 2 (13:34):
Yes, happier customers, more efficient operations, better bottom lines. If
you look at the historical arc, AI in business has
gone from being kind of experimental to being pretty central
for many industries.
Speaker 1 (13:45):
Can you break down some specific applications like how are
businesses actually using it day to day?
Speaker 2 (13:50):
Sure? Data analysis is a massive one as I mentioned, Yeah,
AI can find those needles in the haystack, valuable insights
hidden in vast amounts of data that would just be
noise to a human analyst.
Speaker 1 (14:01):
Right processing power.
Speaker 2 (14:02):
Exactly, automations and other key area think about tasks like
data entry, scheduling, basic customer inquiries. AI can handle a
lot of that, freeing up human staff for things requiring creativity,
empathy or complex problem solving.
Speaker 1 (14:15):
Makes sense? What else?
Speaker 2 (14:16):
And customer service is huge too. Chatbots virtual assistance. They
can provide instant twenty four to seven support, answer common questions,
guide users through processes. This can seriously improve customer satisfaction
and reduce way times.
Speaker 1 (14:30):
Okay, I can see how that would be attractive for
a business.
Speaker 2 (14:33):
Absolutely so if we bring it all together, AI is
constantly analyzing data and making these suggestions or automating processes.
This subtly shapes the choices we make as consumers, what
we see, what we buy, how we get information, and
for businesses, it's giving them powerful tools to understand us
their customers better than ever before and hopefully create products
(14:54):
and services that we actually want or need.
Speaker 1 (14:56):
It really connects back to that data being the fuel.
Speaker 2 (14:58):
It always comes back to the data. And you know,
for anyone listening who's curious about diving deeper into how
AI could specifically help them or their business, there are
loads of resources online now, websites, tools, articles that offer
more specific insights.
Speaker 1 (15:12):
Good tip. Okay, let's try and wrap this up. We've
covered a lot of ground in this deep dive. It
feels like the main takeaway is just how pervasive AI
has become. It's this incredibly widespread, influential force in our
daily lives, but often operating you know, almost invisibly behind
the scenes.
Speaker 2 (15:28):
Absolutely from the moment you pick up your phone in
the morning, through your commute your work, you're online shopping,
how you unwind in the evening. AI is likely playing
a role subtly guiding choices and shaping experiences.
Speaker 1 (15:41):
Yeah, it's really not some distant futuristic idea anymore, is it.
It's very much a present day reality, and it is
simplifying and streamlining lots of things.
Speaker 2 (15:49):
It definitely offers significant benefits in convenience efficiency, personalization, though,
as we discuss, it's also fair to have conversations about
things like privacy bias and that risk of overreliance.
Speaker 1 (16:02):
Important caveats, But the bottom line is AI is here,
and it's deeply integrated.
Speaker 2 (16:07):
And looking ahead, it seems almost certain that its role
in our everyday decisions is only going to grow. It'll
likely become even more seamless, even more integrated.
Speaker 1 (16:16):
So maybe a final thought for everyone listening today just
try this, Yeah, think about one specific instance from your
day so far, just one moment where, now that you've
heard all this, you realize AI probably played a part
in a decision you made or an experience you had.
Speaker 2 (16:31):
Hmm, yeah, good exercise.
Speaker 1 (16:33):
It could be tiny a song recommendation you skipped or
listened to and add that seemed relevant that slightly quicker
route your GPS found. Just pick one thing and pause
on it for a second.
Speaker 2 (16:44):
It might just shift your perspective a little bit on
your own relationship with this technology that's quietly reshaping our world.
Speaker 1 (16:50):
Exactly something to mull over. Thanks for that fascinating discussion,
My pleasure.
Speaker 2 (16:55):
Lots to think about.
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